KF (Kalman Filtering) is an optimal filtering technique commonly utilized for estimating the state variables of a system. Kalman filtering is a time domain operation that is suitable for use in estimating the state variables of time-varying systems that can be described by a set of differential equations with time-varying coefficients. Kalman filtering approaches can be utilized for estimating unknown inputs of specific classes by augmenting the process model by an appropriate input generator.
State estimation in large-scale, discrete-time systems may be modeled as a network of interconnected subsystems. Particular examples of those systems may be plant-wide technological systems consisting of several units that are coupled via media/energy flows or spatially discretized models of distributed parameter systems. One approach to state estimation, i.e., Kalman filtering, is highly resource demanding (on cpu time, memory). The problem is to obtain a stable suboptimal algorithm distributed among several processing units whose number and connection topology corresponds to that of the process model. Distributing the computation load among several computing units is one issue. Another, equally important problem is reducing the overall communication among the units. Distributed implementations of Kalman filter require the units to exchange data not only with their respective neighbors in the network, but also with all other units, creating thus a significant communication overhead, and a potential failure mode when the communication link between local units is broken.